I am starting to use Bokeh to plot data that does not share a common x or y variable. I would like to be able to select a line and have the other, non-selected lines, grey out. Ideally the selected line would also be brought to the front of the plot.
So far I been able to get the line selected, but I can't find a way of "greying out" the non-selected lines, or setting the level of the selected line.
import numpy as np
from bokeh.plotting import figure, show, output_file
from bokeh.models.sources import ColumnDataSource
from bokeh.models import Line,TapTool
output_file("test.html")
x0s = np.random.randint(0,20,20)
y0s = np.random.randint(0,20,20)
x1s = np.random.randint(0,20,20)
y1s = np.random.randint(0,20,20)
p_left = figure(tools=[TapTool()])
for xs,ys in zip([x0s,x1s],[y0s,y1s]):
source = ColumnDataSource({'x': xs, 'y': ys})
default_line = Line(x='x', y='y', line_color='blue', line_width=2)
selected_line = Line(line_color='red', line_width=4)
nonselected_line = Line(line_color='grey')
p_left.add_glyph(source,default_line,selection_glyph=selected_line,nonselection_glyph=nonselected_line)
show(p_left)
I'm in a similar situation, and found this example:
https://docs.bokeh.org/en/latest/docs/user_guide/styling.html#selected-and-unselected-glyphs
Haven't tried it out myself, but seems to be close to what you're looking for.
EDIT Just tried it, worked flawlessly for me.
Related
I'm trying to get xy coordinates of points drawn by the user. I want to have them as a dictionary, a list or a pandas DataFrame.
I'm using Bokeh 2.0.2 in Jupyter. There'll be a background image (which is not the focus of this post) and on top, the user will create points that I could use further.
Below is where I've managed to get to (with some dummy data). And I've commented some lines which I believe are the direction in which I'd have to go. But I don't seem to get the grasp of it.
from bokeh.plotting import figure, show, Column, output_notebook
from bokeh.models import PointDrawTool, ColumnDataSource, TableColumn, DataTable
output_notebook()
my_tools = ["pan, wheel_zoom, box_zoom, reset"]
#create the figure object
p = figure(title= "my_title", match_aspect=True,
toolbar_location = 'above', tools = my_tools)
seeds = ColumnDataSource({'x': [2,14,8], 'y': [-1,5,7]}) #dummy data
renderer = p.scatter(x='x', y='y', source = seeds, color='red', size=10)
columns = [TableColumn(field="x", title="x"),
TableColumn(field="y", title="y")]
table = DataTable(source=seeds, columns=columns, editable=True, height=100)
#callback = CustomJS(args=dict(source=seeds), code="""
# var data = source.data;
# var x = data['x']
# var y = data['y']
# source.change.emit();
#""")
#
#seeds.x.js_on_change('change:x', callback)
draw_tool = PointDrawTool(renderers=[renderer])
p.add_tools(draw_tool)
p.toolbar.active_tap = draw_tool
show(Column(p, table))
From the documentation at https://docs.bokeh.org/en/latest/docs/user_guide/tools.html#pointdrawtool:
The tool will automatically modify the columns on the data source corresponding to the x and y values of the glyph. Any additional columns in the data source will be padded with the declared empty_value, when adding a new point. Any newly added points will be inserted on the ColumnDataSource of the first supplied renderer.
So, just check the corresponding data source, seeds in your case.
The only issue here is if you want to know exactly what point has been changed or added. In this case, the simplest solution would be to create a custom subclass of PointDrawTool that does just that. Alternatively, you can create an additional "original" data source and compare seeds to it each time it's updated.
The problem is that the execute it in Python. But show create a static version. Here is a simple example that fix it! I removed the table and such to make it a bit cleaner, but it will also work with it:
from bokeh.plotting import figure, show, output_notebook
from bokeh.models import PointDrawTool
output_notebook()
#create the figure object
p = figure(width=400,height=400)
renderer = p.scatter(x=[0,1], y=[1,2],color='red', size=10)
draw_tool = PointDrawTool(renderers=[renderer])
p.add_tools(draw_tool)
p.toolbar.active_tap = draw_tool
# This part is imporant
def app(doc):
global p
doc.add_root(p)
show(app) #<-- show app and not p!
I have more than one line on a bokeh plot, and I want the HoverTool to show the value for each line, but using the method from a previous stackoverflow answer isn't working:
https://stackoverflow.com/a/27549243/3087409
Here's the relevant code snippet from that answer:
fig = bp.figure(tools="reset,hover")
s1 = fig.scatter(x=x,y=y1,color='#0000ff',size=10,legend='sine')
s1.select(dict(type=HoverTool)).tooltips = {"x":"$x", "y":"$y"}
s2 = fig.scatter(x=x,y=y2,color='#ff0000',size=10,legend='cosine')
fig.select(dict(type=HoverTool)).tooltips = {"x":"$x", "y":"$y"}
And here's my code:
from bokeh.models import HoverTool
from bokeh.plotting import figure
source = ColumnDataSource(data=dict(
x = [list of datetimes]
wind = [some list]
coal = [some other list]
)
)
hover = HoverTool(mode = "vline")
plot = figure(tools=[hover], toolbar_location=None,
x_axis_type='datetime')
plot.line('x', 'wind')
plot.select(dict(type=HoverTool)).tooltips = {"y":"#wind"}
plot.line('x', 'coal')
plot.select(dict(type=HoverTool)).tooltips = {"y":"#coal"}
As far as I can tell, it's equivalent to the code in the answer I linked to, but when I hover over the figure, both hover tools boxes show the same value, that of the wind.
You need to add renderers for each plot. Check this. Also do not use samey label for both values change the names.
from bokeh.models import HoverTool
from bokeh.plotting import figure
source = ColumnDataSource(data=df)
plot = figure(x_axis_type='datetime',plot_width=800, plot_height=300)
plot1 =plot.line(x='x',y= 'wind',source=source,color='blue')
plot.add_tools(HoverTool(renderers=[plot1], tooltips=[('wind',"#wind")],mode='vline'))
plot2 = plot.line(x='x',y= 'coal',source=source,color='red')
plot.add_tools(HoverTool(renderers=[plot2], tooltips=[("coal","#coal")],mode='vline'))
show(plot)
The output look like this.
I would like to set the color of a Bokeh line plot (Bokeh version 0.12.5) using a ColumnDataSource. However, with a line plot nothing is plotted. On the other hand, if I use a circle renderer everything works as expected. Below is an example program with both a line plot and a circle plot and you can comment/uncomment the appropriate lines to see the plotting behavior. I also included a line of code for a line plot where the color is explicitly defined and the plot works perfectly. I have seen a couple similar questions asked but could not find a solid solution to this problem or determine if I am just doing something fundamentally wrong. Thanks for your help.
# bokeh version 0.12.5
# run in terminal with: python -m bokeh serve --show line_plot_color.py
from bokeh.io import curdoc
from bokeh.models import ColumnDataSource
from bokeh.plotting import Figure
from bokeh.layouts import row
source = ColumnDataSource(data = dict(color = ['green','green','green'], xs = [1,2,3], ys = [1,2,3]))
fig = Figure(plot_width=300, plot_height=300)
#r = fig.circle('xs','ys',source = source, size = 12, fill_color = 'color') # works as expected
r = fig.line('xs','ys',source = source, line_color = 'color') # fails to plot; no errors or warnings in terminal
#r = fig.line('xs','ys',source = source, line_color = 'green') # works as expected
layout = row(fig)
curdoc().add_root(layout)
First to help you with debugging the bokeh server, it's very useful to use the devtools that come with web browsers. The devtools' console will contain useful debugging information as is the case for your example.
Second, looking through the docs the line glyph method is not set up to receive a column data source value for its coloring. If you want to plot multiple lines with different colors on a single figure, then you can use the multi_line glyph. To use this glyph, you need to modify your data source xs and ys to be a list of list for each line in your multi_line. Here's a quick example.
source2 = ColumnDataSource(data = dict(color = ['green','red'], xs = [[1, 2],[2, 4]], ys = [[1, 2],[2, 4]]))
r = fig.multi_line('xs','ys',source = source2, line_color = 'color')
I have got a pandas dataframe whose columns I want to show as lines in a plot using a Bokeh server. Additionally, I would like to have a slider for shifting one of the lines against the other.
My problem is the update functionality when the slider value changes. I have tried the code from the sliders-example of bokeh, but it does not work.
Here is an example
import pandas as pd
from bokeh.io import vform
from bokeh.plotting import Figure, output_file, show
from bokeh.models import CustomJS, ColumnDataSource, Slider
df = pd.DataFrame([[1,2,3],[3,4,5]])
df = df.transpose()
myindex = list(df.index.values)
mysource = ColumnDataSource(df)
plot = Figure(plot_width=400, plot_height=400)
for i in range(len(mysource.column_names) - 1):
name = mysource.column_names[i]
plot.line(x = myindex, y = str(name), source = mysource)
offset = Slider(title="offset", value=0.0, start=-1.0, end=1.0, step=1)
def update_data(attrname, old, new):
# Get the current slider values
a = offset.value
temp = df[1].shift(a)
#to finish#
offset.on_change('value', update_data)
layout = vform(offset, plot)
show(layout)
Inside the update_data-function I have to update mysource, but I cannot figure out how to do that. Can anybody point me in the right direction?
Give this a try... change a=offset.value to a=cb_obj.get('value')
Then put source.trigger('change') after you do whatever it is you are trying to do in that update_data function instead of offset.on_change('value', update_data).
Also change offset = Slider(title="offset", value=0.0, start=-1.0, end=1.0, step=1, callback=CustomJS.from_py_func(offset))
Note this format I'm using works with flexx installed. https://github.com/zoofio/flexx if you have Python 3.5 you'll have to download the zip file, extract, and type python setup.py install as it isn't posted yet compiled for this version...
I'm new to bokeh and I just jumped right into using hovertool as that's why I wanted to use bokeh in the first place.
Now I'm plotting genes and what I want to achieve is multiple lines with the same y-coordinate and when you hover over a line you get the name and position of this gene.
I have tried to mimic this example, but for some reason the I can't even get it to show coordinates.
I'm sure that if someone who actually knows their way around bokeh looks at this code, the mistake will be apparent and I'd be very thankful if they showed it to me.
from bokeh.plotting import figure, HBox, output_file, show, VBox, ColumnDataSource
from bokeh.models import Range1d, HoverTool
from collections import OrderedDict
import random
ys = [10 for x in range(len(levelsdf2[(name, 'Start')]))]
xscale = zip(levelsdf2[('Log', 'Start')], levelsdf2[('Log', 'Stop')])
yscale = zip(ys,ys)
TOOLS="pan,wheel_zoom,box_zoom,reset,hover"
output_file("scatter.html")
hover_tips = levelsdf2.index.values
colors = ["#%06x" % random.randint(0,0xFFFFFF) for c in range(len(xscale))]
source = ColumnDataSource(
data=dict(
x=xscale,
y=yscale,
gene=hover_tips,
colors=colors,
)
)
p1 = figure(plot_width=1750, plot_height=950,y_range=[0, 15],tools=TOOLS)
p1.multi_line(xscale[1:10],yscale[1:10], alpha=1, source=source,line_width=10, line_color=colors[1:10])
hover = p1.select(dict(type=HoverTool))
hover.tooltips = [
("index", "$index"),
("(x,y)", "($x, $y)"),
]
show(p1)
the levelsdf2 is a pandas.DataFrame, if it matters.
I figured it out on my own. It turns out that version 0.8.2 of Bokeh doesn't allow hovertool for lines so I did the same thing using quads.
from bokeh.plotting import figure, HBox, output_file, show, VBox, ColumnDataSource
from bokeh.models import Range1d, HoverTool
from collections import OrderedDict
import random
xscale = zip(levelsdf2[('series1', 'Start')], levelsdf2[('series1', 'Stop')])
xscale2 = zip(levelsdf2[('series2', 'Start')], levelsdf2[('series2', 'Stop')])
yscale2 = zip([9.2 for x in range(len(levelsdf2[(name, 'Start')]))],[9.2 for x in range(len(levelsdf2[(name, 'Start')]))])
TOOLS="pan,wheel_zoom,box_zoom,reset,hover"
output_file("linesandquads.html")
hover_tips = levelsdf2.index.values
colors = ["#%06x" % random.randint(0,0xFFFFFF) for c in range(len(xscale))]
proc1 = 'Log'
proc2 = 'MazF2h'
expression1 = levelsdf2[(proc1, 'Level')]
expression2 = levelsdf2[(proc2, 'Level')]
source = ColumnDataSource(
data=dict(
start=[min(xscale[x]) for x in range(len(xscale))],
stop=[max(xscale[x]) for x in range(len(xscale))],
start2=[min(xscale2[x]) for x in range(len(xscale2))],
stop2=[max(xscale2[x]) for x in range(len(xscale2))],
gene=hover_tips,
colors=colors,
expression1=expression1,
expression2=expression2,
)
)
p1 = figure(plot_width=900, plot_height=500,y_range=[8,10.5],tools=TOOLS)
p1.quad(left="start", right="stop", top=[9.211 for x in range(len(xscale))],
bottom = [9.209 for x in range(len(xscale))], source=source, color="colors")
p1.multi_line(xscale2,yscale2, source=source, color="colors", line_width=20)
hover = p1.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
(proc1+" (start,stop, expression)", "(#start| #stop| #expression1)"),
("Gene","#gene"),
])
show(p1)
Works like a charm.
EDIT: Added a picture of the result, as requested and edited code to match the screenshot posted.
It's not the best solution as it turns out it's not all that easy to plot several series of quads on one plot. It's probably possible but as it didn't matter much in my use case I didn't investigate too vigorously.
As all genes are represented on all series at the same place I just added tooltips for all series to the quads and plotted the other series as multi_line plots on the same figure.
This means that if you hovered on the top line at 9.21 you'd get tooltips for the line at 9.2 as well, but If you hovered on the 9.2 line you wouldn't get a tooltip at all.